Characterization of Fungal Surface Contaminants of the Small Maltese June Pear, Pyrus communis var. bambinella.

ABSTRACT: Fungal pathogens cause surface contamination and potential premature fruit spoilage of bambinella, a fruit endemic to the Maltese islands, leading to the loss of fruit during the postharvest phase. The objective of this study was to isolate, quantify, and characterize fungal contaminants of the small Maltese June Pear and describe their growth kinetics. In total, 284 fungicide-free fruits were collected over three consecutive summers (2014, 2015, 2016). The isolated fungi were identified by using forward and reverse colonial morphology. Species identification was determined using PCR-based methods. The number of CFU per square centimeter of bambinella outer skin was calculated. Mycelium diameter growth rate studies of the isolates were also carried out at seven different temperatures, ranging from 5 to 35°C. Fungi isolated from bambinella included Cladosporium ramotenellum, Alternaria arborescens, Penicillium lanosum, Penicillium expansum, and Aspergillus sydowii, listed from the most abundant to the least abundant. The Rosso model was fitted to the growth kinetic data and showed that the optimal temperatures for growth of all five fungi were in the range of 20 to 22°C, whereas growth was slower at temperatures below 10°C and above 30°C. As observed in the diameter studies, the order of highest to lowest germination rate was found to be P. expansum, A. sydowii, P. lanosum, C. ramotenellum, and A. arborescens. Germination studies showed that the highest germination rate was observed for P. lanosum, followed by A. arborescens, C. ramotenellum, P. expansum, and A. sydowii, in descending order. The highest germination lag time was observed for A. arborescens, followed by C. ramotenellum, P. expansum, P. lanosum, and A. sydowii, in ascending order.

Risk assessment of fungal spoilage: A case study of Aspergillus niger on yogurt.

A quantitative risk assessment model of yogurt spoilage by Aspergillus niger was developed based on a stochastic modeling approach for mycelium growth by taking into account the important sources of variability such as time-temperature conditions during the different stages of chill chain and individual spore behavior. Input parameters were fitted to the appropriate distributions and A. niger colony's diameter at each stage of the chill chain was estimated using Monte Carlo simulation. By combining the output of the growth model with the fungus prevalence, that can be estimated by the industry using challenge tests, the risk of spoilage translated to number of yogurt cups in which a visible mycelium of A. niger is being formed at the time of consumption was assessed. The risk assessment output showed that for a batch of 100,000 cups in which the percentage of contaminated cups with A. niger was 1% the predicted numbers (median (5th, 95th percentiles)) of the cups with a visible mycelium at consumption time were 8 (5, 14). For higher percentages of 3, 5 and 10 the predicted numbers (median (5th, 95th percentiles)) of the spoiled cups at consumption time were estimated to be 24 (16, 35), 39 (29, 52) and 80 (64, 94), respectively. The developed model can lead to a more effective risk-based quality management of yogurt and support the decision making in yogurt production.

Quantifying the effect of water activity and storage temperature on single spore lag times of three moulds isolated from spoiled bakery products.

The inhibitory effect of water activity (aw) and storage temperature on single spore lag times of Aspergillus niger, Eurotium repens (Aspergillus pseudoglaucus) and Penicillium corylophilum strains isolated from spoiled bakery products, was quantified. A full factorial design was set up for each strain. Data were collected at levels of aw varying from 0.80 to 0.98 and temperature from 15 to 35°C. Experiments were performed on malt agar, at pH5.5. When growth was observed, ca 20 individual growth kinetics per condition were recorded up to 35days. Radius of the colony vs time was then fitted with the Buchanan primary model. For each experimental condition, a lag time variability was observed, it was characterized by its mean, standard deviation (sd) and 5th percentile, after a Normal distribution fit. As the environmental conditions became stressful (e.g. storage temperature and aw lower), mean and sd of single spore lag time distribution increased, indicating longer lag times and higher variability. The relationship between mean and sd followed a monotonous but not linear pattern, identical whatever the species. Next, secondary models were deployed to estimate the cardinal values (minimal, optimal and maximal temperatures, minimal water activity where no growth is observed anymore) for the three species. That enabled to confirm the observation made based on raw data analysis: concerning the temperature effect, A. niger behaviour was significantly different from E. repens and P. corylophilum: Topt of 37.4°C (standard deviation 1.4°C) instead of 27.1°C (1.4°C) and 25.2°C (1.2°C), respectively. Concerning the aw effect, from the three mould species, E. repens was the species able to grow at the lowest aw (awmin estimated to 0.74 (0.02)). Finally, results obtained with single spores were compared to findings from a previous study carried out at the population level (Dagnas et al., 2014). For short lag times (≤5days), there was no difference between lag time of the population (ca 2000 spores inoculated in one spot) and mean (nor 5th percentile) of single spore lag time distribution. In contrast, when lag time was longer, i.e. under more stressful conditions, there was a discrepancy between individual and population lag times (population lag times shorter than 5th percentiles of single spore lag time distribution), confirming a stochastic process. Finally, the temperature cardinal values estimated with single spores were found to be similar to those obtained at the population level, whatever the species. All these findings will be used to describe better mould spore lag time variability and then to predict more accurately bakery product shelf-life.

Development and application of Geobacillus stearothermophilus growth model for predicting spoilage of evaporated milk.

The presence of Geobacillus stearothermophilus spores in evaporated milk constitutes an important quality problem for the milk industry. This study was undertaken to provide an approach in modelling the effect of temperature on G. stearothermophilus ATCC 7953 growth and in predicting spoilage of evaporated milk. The growth of G. stearothermophilus was monitored in tryptone soy broth at isothermal conditions (35-67 °C). The data derived were used to model the effect of temperature on G. stearothermophilus growth with a cardinal type model. The cardinal values of the model for the maximum specific growth rate were Tmin = 33.76 °C, Tmax = 68.14 °C, Topt = 61.82 °C and µopt = 2.068/h. The growth of G. stearothermophilus was assessed in evaporated milk at Topt in order to adjust the model to milk. The efficiency of the model in predicting G. stearothermophilus growth at non-isothermal conditions was evaluated by comparing predictions with observed growth under dynamic conditions and the results showed a good performance of the model. The model was further used to predict the time-to-spoilage (tts) of evaporated milk. The spoilage of this product caused by acid coagulation when the pH approached a level around 5.2, eight generations after G. stearothermophilus reached the maximum population density (Nmax). Based on the above, the tts was predicted from the growth model as the sum of the time required for the microorganism to multiply from the initial to the maximum level ( [Formula: see text] ), plus the time required after the [Formula: see text] to complete eight generations. The observed tts was very close to the predicted one indicating that the model is able to describe satisfactorily the growth of G. stearothermophilus and to provide realistic predictions for evaporated milk spoilage.

Modeling red cabbage seed extract effect on Penicillium corylophilum: Relationship between germination time, individual and population lag time.

The inhibitory effect of a red cabbage seed extract on germination time, individual (single spore) and population lag time of Penicillium corylophilum was studied. First, to compare the biological variability of single spore germination and lag times under stressful conditions, data were collected at levels of red cabbage seed extract varying from 0 to 10 mg/g (150 spores observed in each trial of germination, ca 50 spores in each individual lag experiment). Experiments were performed on malt agar at 25 °C, pH 5.2, aw 0.99. The data, without any transformation, were statistically analyzed; several probability distribution functions were used to fit the cumulated germination times and the individual lag times of spores. In both cases, the best fit was obtained with the Normal distribution. In parallel, lag times at the population level (ca 2000 spores per trial) were collected for the same range of plant extract. Not surprisingly, the difference between individual and population lag times could be explained by a stochastic process. More interestingly, it was shown that under stressful conditions, the population lag time did not correspond to the time required for germination of 95% of spores, but to a much longer time. Finally, it was deduced from the statistical analysis, completed by microscopic observations, that the plant extract affected mainly the hyphal elongation (and then the lag time) and not the germination. Next, secondary models were developed to quantify the effect of red cabbage seed extract on the median of germination times, individual and population lag times. The Minimum Inhibitory Concentrations (MICs) were estimated. It was shown that the red cabbage seed extract MIC for P. corylophilum lag time did not depend on the inoculum load. Application of the secondary models allowed us to conclude that under the conditions of our experiment, the addition of 10 mg/g of red cabbage seed extract enabled extension of lag time to two weeks.

Relation between germination and mycelium growth of individual fungal spores.

The relation between germination time and lag time of mycelium growth of individual spores was studied by combining microscopic and macroscopic techniques. The radial growth of a large number (100-200) of Penicillium expansum and Aspergillus niger mycelia originating from single spores was monitored macroscopically at isothermal conditions ranging from 0 to 30°C and 10 to 41.5°C, respectively. The radial growth curve for each mycelium was fitted to a linear model for the estimation of mycelium lag time. The results showed that the lag time varied significantly among single spores. The cumulative frequency distributions of the lag times were fitted to the modified Gompertz model and compared with the respective distributions for the germination time, which were obtained microscopically. The distributions of the measured mycelium lag time were found to be similar to the germination time distributions under the same conditions but shifted in time with the lag times showing a significant delay compared to germination times. A numerical comparison was also performed based on the distribution parameters λ(m) and λ(g), which indicate the time required from the spores to start the germination process and the completion of the lag phase, respectively. The relative differences %(λ(m)-λ(g))/λ(m) were not found to be significantly affected by temperatures tested with mean values of 72.5±5.1 and 60.7±2.1 for P. expansum for A. niger, respectively. In order to investigate the source of the above difference, a time-lapse microscopy method was developed providing videos with the behavior of single fungal spore from germination until mycelium formation. The distances of the apexes of the first germ tubes that emerged from the swollen spore were measured in each frame of the videos and these data were expressed as a function of time. The results showed that in the early hyphal development, the measured radii appear to increase exponentially, until a certain time, where growth becomes linear. The two phases of hyphal development can explain the difference between germination and lag time. Since the lag time is estimated from the extrapolation of the regression line of the linear part of the graph only, its value is significantly higher than the germination time, t(G). The relation of germination and lag time was further investigated by comparing their temperature dependence using the Cardinal Model with Inflection. The estimated values of the cardinal parameters (T(min), T(opt), and T(max)) for 1/λ(g) were found to be very close to the respective values for 1/λ(m), indicating similar temperature dependence between them.

Modeling germination of fungal spores at constant and fluctuating temperature conditions.

The germination of Penicillium expansum and Aspergillus niger spores was monitored microscopically on malt extract agar at isothermal conditions ranging from 0 to 33°C and 5 to 41.5°C, respectively. The obtained germination data, expressed as percentage of germination (% P) versus time, were fitted to the modified Gompertz equation for the estimation of the germination kinetic parameters (lag time, λ(g), and germination rate, µ(g)), which were further modeled as a function of temperature via the use of Cardinal Models with Inflection (CMI). The effect of temperature on these parameters was similar with that previously reported for mycelium growth kinetics of the tested isolates. The germination of spores was also studied at various dynamic time-temperature conditions including single or sequential temperature shifts. The germination of spores at fluctuating temperatures was predicted using the modified Gompertz equation in conjunction with the CMI models for λ(g) and µ(g) and based on the assumptions that i) a temperature shift does not result in any additional λ(g) and, thus, the total lag can be calculated by adding relative parts of the lag time, and ii) after a temperature shift the germination rate µ(g) adapts instantaneously to the new temperature. The comparison between predicted and observed data showed that the germination of spores is strongly affected by the extent of the temperature shift, the percentage of germinated spores at the time of the shift and the fungal species. Apart from the scientific interest in understanding the dynamics of spores' germination, the models developed in this study can be used as tools in effective quality management systems for fungi control in foods.

Development and application of predictive models for fungal growth as tools to improve quality control in yogurt production.

The effect of storage temperature (0-40 °C) and inoculum size (10¹-105 spores) on the mycelium growth kinetics of 12 fungal species on yogurt were monitored. A cardinal model with inflection (CMI) was used to describe the effect of temperature on the growth rate (µ) and the lag time (λ) of each isolate. Significant differences on the temperature dependence of the mycelium growth between the tested species were observed. Depending on the strain, the estimated minimum, optimum and maximum temperature parameters for µ (T(min), T(opt), T(max)) ranged from -7.6 to 9.6, 19.5 to 37.8 and 29.8 to 46.9 °C, respectively. Only λ was found to be affected by the inoculum size and a linear relation between Ln (λ) and Log (inoculum size) was revealed. The inoculum level did not influence the values of T(min), T(opt) and T(max) for λ. Based on the above observations, the combined effect of inoculum size and temperature on λ was modeled using a modified CMI. The parameter λ(opt) (λ at optimum conditions) was expressed as a function of the inoculum size. Validation studies showed a good performance of the developed models. The application scheme of the models for improving fungi control in yogurt productions is discussed.

Modelling growth of Penicillium expansum and Aspergillus niger at constant and fluctuating temperature conditions.

The growth of Penicillium expansum and Aspergillus niger, isolated from yogurt production environment, was investigated on malt extract agar with pH=4.2 and a(w)=0.997, simulating yogurt, at isothermal conditions ranging from -1.3 to 35 degrees C and from 5 to 42.3 degrees C, respectively. The growth rate (mu) and (apparent) lag time (lambda) of the mycelium growth were modelled as a function of temperature using a Cardinal Model with Inflection (CMI). The results showed that the CMI can describe successfully the effect of temperature on fungal growth within the entire biokinetic range for both isolates. The estimated values of the CMI for mu were T(min)=-5.74 degrees C, T(max)=30.97 degrees C, T(opt)=22.08 degrees C and mu(opt)=0.221 mm/h for P. expansum and T(min)=10.13 degrees C, T(max)=43.13 degrees C, T(opt)=31.44 degrees C, and mu(opt)=0.840 mm/h for A. niger. The cardinal values for lambda were very close to the respective values for mu indicating similar temperature dependence of the growth rate and the lag time of the mycelium growth. The developed models were further validated under fluctuating temperature conditions using various dynamic temperature scenarios. The time-temperature conditions studied included single temperature shifts before or after the end of the lag time and continuous periodic temperature fluctuations. The prediction of growth at changing temperature was based on the assumption that after a temperature shift the growth rate is adopted instantaneously to the new temperature, while the lag time was predicted using a cumulative lag approach. The results showed that when the temperature shifts occurred before the end of the lag, they did not cause any significant additional lag and the observed total lag was very close to the cumulative lag predicted by the model. In experiments with temperature shifts after the end of the lag time, accurate predictions were obtained when the temperature profile included temperatures which were inside the region of growth, showing that the assumption that mu is adopted instantaneously to the current temperature is concrete. In contrast, for scenarios with temperatures close or outside the growth region the models overestimated growth, indicating that fungi were stressed by this type of temperature shifts. The present study provides useful data for understanding the behavior of P. expansum and A. niger at dynamic temperature conditions while the developed models can be used as effective tools in assessing the risk of fungal spoilage and predicting shelf life of foods.